https://github.com/gszfwsb/NCFM
Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function" (NCFM) in CVPR 2025.
https://github.com/gszfwsb/NCFM
computer-vision data-centric-ai dataset-distillation synthetic-data
Last synced: about 1 month ago
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Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function" (NCFM) in CVPR 2025.
- Host: GitHub
- URL: https://github.com/gszfwsb/NCFM
- Owner: gszfwsb
- Created: 2025-03-01T16:25:09.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-10T14:50:12.000Z (about 2 months ago)
- Last Synced: 2025-03-10T15:22:46.009Z (about 2 months ago)
- Topics: computer-vision, data-centric-ai, dataset-distillation, synthetic-data
- Language: Python
- Homepage:
- Size: 1.16 MB
- Stars: 279
- Watchers: 2
- Forks: 15
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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- awesome-hacking-lists - gszfwsb/NCFM - Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function: A Minmax Perspective" (NCFM) in CVPR 2025. (Python)
README
# [CVPR2025] Dataset Distillation with Neural Characteristic Function: A Minmax Perspective
Official PyTorch implementation of the paper ["Dataset Distillation with Neural Characteristic Function"](https://arxiv.org/abs/2502.20653) (NCFM) in CVPR 2025.
## :fire: News
- [2025/03/02] The code of our paper has been released.
- [2025/02/27] Our NCFM paper has been accepted to CVPR 2025 (Rating: 555). Thanks!
## :rocket: Pipeline
Here's an overview of the process behind our **Neural Characteristic Function Matching (NCFM)** method:
## :mag: TODO
**We are currently organizing all the code. Stay tuned!**
- [ ] Distillation code
- [x] Evaluation code
- [ ] Sampling network
- [ ] Config files
- [ ] Pretrained models
- [ ] Distilled datasets
- [ ] Continual learning code
- [ ] Project page
## 🛠️ Getting Started
To get started with NCFM, follow the installation instructions below.
1. Clone the repo
```sh
git clone https://github.com/gszfwsb/NCFM.git
```
2. Install dependencies
```sh
pip install -r requirements.txt
```
3. Pretrain the models yourself, or download the **pretrained_models** from [Google Drive](https://drive.google.com/drive/folders/1HT_eUbTWOVXvBov5bM90b169jdy2puOh?usp=drive_link).
```sh
cd pretrain
torchrun --nproc_per_node={n_gpus} --nnodes=1 pretrain_script.py --gpu={gpu_ids} --config_path=../config/{ipc}/{dataset}.yaml
```
4. Condense
```sh
cd condense
torchrun --nproc_per_node={n_gpus} --nnodes=1 condense_script.py --gpu={gpu_ids} --ipc={ipc} --config_path=../config/{ipc}/{dataset}.yaml
```
5. Evaluation
```sh
cd evaluation
torchrun --nproc_per_node={n_gpus} --nnodes=1 evaluation_script.py --gpu={gpu_ids} --ipc={ipc} --config_path=../config/{ipc}/{dataset}.yaml --load_path={distilled_dataset.pt}
```
### :blue_book: Example Usage
1. CIFAR-10
```sh
#ipc50
cd condense
torchrun --nproc_per_node=8 --nnodes=1 --master_port=34153 condense_script.py --gpu="0,1,2,3,4,5,6,7" --ipc=50 --config_path=../config/ipc50/cifar10.yaml
```
2. CIFAR-100
```sh
#ipc10
cd condense
torchrun --nproc_per_node=8 --nnodes=1 --master_port=34153 condense_script.py --gpu="0,1,2,3,4,5,6,7" --ipc=10 --config_path=../config/ipc10/cifar100.yaml
```
## :postbox: Contact
If you have any questions, please contact [Shaobo Wang](https://gszfwsb.github.io/)(`[email protected]`).
## :pushpin: Citation
If you find NCFM useful for your research and applications, please cite using this BibTeX:
```bibtex
@misc{wang2025datasetdistillationneuralcharacteristic,
title={Dataset Distillation with Neural Characteristic Function: A Minmax Perspective},
author={Shaobo Wang and Yicun Yang and Zhiyuan Liu and Chenghao Sun and Xuming Hu and Conghui He and Linfeng Zhang},
year={2025},
eprint={2502.20653},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2502.20653},
}
```
## Acknowledgement
We sincerely thank the developers of the following projects for their valuable contributions and inspiration: [MTT](https://github.com/GeorgeCazenavette/mtt-distillation), [DATM](https://github.com/NUS-HPC-AI-Lab/DATM), [DC/DM](https://github.com/VICO-UoE/DatasetCondensation), [IDC](https://github.com/snu-mllab/Efficient-Dataset-Condensation), [SRe2L](https://github.com/VILA-Lab/SRe2L), [RDED](https://github.com/LINs-lab/RDED), [DANCE](https://github.com/Hansong-Zhang/DANCE). We draw inspiration from these fantastic projects!